Climate Classification Research Articles

Threshold Changes in Winter Temperature and Precipitation Drive Threshold Responses Across Nine Global Climate Zones and Associated Biomes

Globally, winter temperatures are rising, and snowpack is shrinking or disappearing entirely. Despite previous research and published literature reviews, it remains unknown whether biomes across the globe will cross important thresholds in winter temperature and precipitation that will lead to significant ecological changes. Here, we combine the widely used Köppen–Geiger climate classification system with worst-case-scenario projected changes in global monthly temperature and precipitation to illustrate how multiple climatic zones across Earth may experience shifting winter conditions by the end of this century. We then examine how these shifts may affect ecosystems within corresponding biomes. Our analysis demonstrates potential widespread losses of extreme cold (<−20°C) in Arctic, boreal, and cool temperate regions. We also show the possible disappearance of freezing temperatures (<0°C) and large decreases in snowfall in warm temperate and dryland areas. We identify important and potentially irreversible ecological changes associated with crossing these winter climate thresholds.

Thermal Transmittance Limits Dataset for New and Existing Buildings Across EU Regulations

Building energy regulations are essential for reducing energy consumption in the European Union (EU) and achieving climate neutrality goals. This data article supplements the “Overview of EU Building Envelope Energy Requirement for Climate Neutrality” by presenting a detailed dataset on building regulations across all 27 EU member states, with a focus on building envelope efficiency. The data include thermal transmittance limits for windows, walls, floors, and roofs, offering insights into regulatory differences and potential opportunities for harmonization. Information was sourced from the Energy Performance of Buildings Directive (EPBD) database, national reports, and scientific literature to ensure comprehensive coverage. Key aspects of each country’s regulations are summarized in tables, covering both new constructions and renovations. The inclusion of Köppen–Geiger climate classifications allows for climate-specific analyses, providing valuable context for researchers, policymakers, and construction professionals. This dataset enables comparative studies, helping to identify best practices and inform policy interventions aimed at enhancing energy efficiency across Europe. It also supports the development of tailored strategies to improve building performance in different environmental conditions, ultimately contributing to the EU’s energy and climate targets.

Passive House Design: A Possible Energy Efficient Option in the Building Sector of Nepal

The diversified geography of Nepal creates huge variations in the country's climatic zones; however, the building industry has so far used standardized methods that tend to neglect local climate conditions. Most of these standards then rely on energy-intensive mechanical systems to maintain indoor thermal comfort, without considering more viable and climate-responsive design methods. In this light, the development of a climate classification related to building design will help develop and encourage energy- and climate-effective building architecture in Nepal. The existing energy-saving practice in the building sector of the country is reviewed in this paper, and it outlines ways to improve the adaptation of energy-efficient methodology. It shows that passive houses are performing much better in comparison with modern constructions that are behind in terms of energy efficiency when compared with traditional homes. The study outlines the climate-specific design criteria and methodologies for various regions and sets the path for exploration of the passive house design process challenges and opportunities that might exist for wider diffusion. Also discussed are strategies to overcome the barriers and promote passive house construction, offering a pathway toward sustainable building practices in Nepal.

Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data

Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth.

Performance assessment of a 30.26 kW grid-connected photovoltaic plant in Egypt

Abstract This paper presents an experimental analysis and performance evaluation of a grid-connected photovoltaic plant installed on the rooftop of the Electronics Research Institute in Cairo, Egypt. Cairo is classified as a hot-desert climate region according to the standard Koppen-Geiger climate classification system. Over a year, we monitored real-time data to assess key system performance metrics, such as energy yield, efficiencies, performance ratio, capacity factor, and losses. Based on the obtained experimental results, the highest final yield of 5.2498 hr/day was observed in the summer, whereas the lowest yield of 3.439 hr/day occurred in the winter months. The photovoltaic plant had an average annual system efficiency of 15.8%, while the photovoltaic and inverter had mean yearly efficiencies of 17.1% and 97.2%, respectively. The average annual performance ratio is 83.03%, and the capacity factor is 18.72%. The monthly total loss exhibited a linear rise alongside increasing ambient temperature and solar irradiance. The ambient temperature affected the system efficiency, photovoltaic efficiency, and performance ratio. The findings can help strengthen forecasts of future large-scale photovoltaic plants in hot desert climates. Moreover, they can guide the design, optimization, operation, and maintenance of new grid-connected photovoltaic systems.

Community structure of tropics emerging from spatio-temporal variations in the Intertropical Convergence Zone dynamics

The Intertropical Convergence Zone (ITCZ) is a narrow tropical belt of deep convective clouds, intense precipitation, and monsoon circulations encircling the Earth. Complex interactions between the ITCZ and local geophysical dynamics result in high climate variability, making weather forecasting and prediction of extreme rainfall or drought events challenging. We unravel the complex spatio-temporal dynamics of the ITCZ and the resulting teleconnection patterns via a novel tropical climate classification achieved using complex network analysis and community detection. We reduce the high-dimensional complex ITCZ dynamics into a simple yet insightful community structure that classifies the tropics into seven regions representing distinct ITCZ dynamics. The two largest communities, encompassing landmasses over the Northern and Southern hemispheres, are associated with coherent seasonal ITCZ dynamics and have significant long-range connections. Temporal analysis of the community structure highlights that the tropical Pacific and Atlantic Oceans communities exhibit substantial variation on multidecadal scales. Further, these communities exhibit incoherent dynamics due to atmosphere-ocean interactions driven by equatorial and coastal oceanic upwelling.

Projection of climate change impact on main climate variables and assessment of the future of Köppen–Geiger climate classification in Iran

AbstractHuman society and the environment are facing significant challenges due to climate change. Climate change is projected to impact main climate variables, such as temperature and precipitation. Changes in main climate variables affect climate classification and alter climate zone maps. In this research, first, the projection of temperature and precipitation in 30 main stations of Iran under the Shared Socioeconomic Pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from Coupled Model Intercomparison Project Phase Six (CMIP6) for the end of the twenty-first century (2071–2099) was carried out. Then, the future of climate zone maps was assessed in Iran by Köppen-Geiger climate classification. The evaluation of the model data based on observation data for the period of 1991–2020 showed an acceptable correlation, with R-square and RMSE values in the ranges of 0.67–0.96 and 2.44–8.38, respectively. Results showed that the temperature in the future period (2071–2099) will increase by 1–4.7 °C under all scenarios compared to the historical study period (1991–2020), while the precipitation will either increase or decrease depending on the season and the specific climate change scenario. Assessment of future climate classifications revealed that the BW (arid desert) and BS (semi-arid steppe) categories will increase, as classified by Köppen-Geiger, will increase. At the same time, Ds and Cs (dry summer) classifications will decrease in during the study period over Iran. These findings provide policymakers with some insights into how to deal with the impacts of climate change in the future and implement some measures now.

Monthly rainfall prediction for different climatic zones in south Africa for 2024 using a random forest model

This study predicted 2024 rainfall in different climatic zones in South Africa using a random forest model. Previous studies have shown that random forests performed better than other models for rainfall prediction. South Africa was divided into nine using the Koppen-Geiger climate classification system, and three cities were selected for each climatic zone. Atmospheric datasets from the South African Weather Service and the National Aeronautics and Space Agency were used for this study. The datasets were trained, tested, and validated to assess the model's accuracy. With good forecast ability observed, the random forest was then used for monthly rainfall for 2024. The result of this prediction was then compared with 2022 and 2023 rainfall. The result indicated months where much rain should be expected in various cities and cities that may likely experience droughts. This result is particularly important for agriculture, water resource management, and early drought/flooding warning systems.

Evaluation of Thermal Comfort Conditions in the Working Environments of Seasonal Agricultural Workers in Csa Koppen Climate Type

This study focuses on determining the thermal comfort conditions of seasonal agricultural workers during the hot periods of the year when agricultural production is intense in the Aksu/Türkiye region, which is characterized by the Csa climate type according to the Köppen–Geiger climate classification. In this study, the thermal comfort conditions of seasonal agricultural workers working on open farmlands were evaluated in ten-day, monthly, and seasonal periods for 6 months between 5:00 and 21:00 h using the modified Physiological Equivalent Temperature (mPET) index in the Rayman Pro software according to their activity energy during work. The results of the study reveal that increased activity energy leads to a decrease in thermal comfort conditions of agricultural workers, mPET values of agricultural workers engaged in soil cultivation (Group II) are 2.1 to 2.9 °C higher than the mPET values of workers engaged in plant care and harvesting (Group I), and the agricultural workers in Group II are exposed to more heat stress. The thermal comfort conditions of agricultural workers in Group I deteriorate between 09:00 and 16:00 h with mPET values between 34.1 and 35.3 °C and those of agricultural workers in Group II deteriorate between 08:00 and 17:00 h with mPET values between 34.3 and 37.7 °C. In this context, the daily comfortable working time in the morning and afternoon was found to be 9 h for Group I and 7 h for Group II. Overall, determining the comfortable working hours of agricultural workers in regions with different climate types in future studies will be an important resource for decision-makers in developing strategies to protect the health and increase the productivity of agricultural workers.

Climate-Responsive Architectural Design: Analyzing and Adapting to Diverse Climates in U.S. Cities

The energy consumption challenge within the built environment is growing in urgency, underscoring an imperative for climate-responsive design solutions. This paper presents an in-depth analysis of the climatic characteristics of 12 U.S. cities, with a parametric platform, categorized under different Köppen climate classifications, with the aim of informing effective passive design strategies. Upon meticulously assessing the efficacy of various passive strategies tailored to distinct climate zones, this paper distills actionable guidance for deploying architectural methods that embody climate-responsive design principles. Theoretical models are developed to correlate climatic variables with specific passive design interventions. The research contributes to the theoretical discourse on passive design by advocation for a data-driven, climate-responsive design guidance that aligns with contemporary sustainability goals.

BIM-based parametric energy analysis of green building components for the roofs and facades

Buildings are distinguished by their high energy demand over their lifespan. Iimproving energy efficiency in buildings necessitates mitigating the operation energy performance by evaluating more sustainable building components such as green infrastructures. The novelty herein is to conduct a BIM-based parametric energy analysis of green building components for the roofs and facades, taking into consideration the climate classification and orientation of the building. The proposed framework of this study uses Autodesk Revit software and Autodesk Green Building Studio as BIM tools, and Minitab software as a parametric analysis tool to estimate the energy performance of several alternative components based on Energy Use Intensity (EUI) and Annual Peak Demand (APD). A case study of a residential building is examined in four cities within different climate zones in Brazil; Rio de Janeiro (climate zone 08), Macaé (climate zone 05), Petrópolis (climate zone 03), and Nova Friburgo (climate zone 02). The base model design of components of the building includes a reinforced concrete roof and ceramic masonry façade. The utilized alternatives are roof components (i.e. green roofs and expanded clay roofs), and green façade. This work insight using green façade for all elevations and green roofs to enhance energy efficiency in terms of EUI by (10,17 %) in Rio de Janeiro, (6,67 %) in Macaé, (11,43 %) in Pertópolis, and (16,43 %) in Nova Friburgo. While the efficiency in terms of APD would be about (28,07 %) in Rio de Janeiro, (22,79 %) in Macaé, (21,67 %) in Pertópolis, and (12,58 %) in Nova Friburgo. When the construction project is designed to install a green façade to the main elevation only, this work recommends using green roofs rather than the expanded clay roof or the base model design roofs to increase energy efficiency in such types of buildings. This work contributes to integrating green building components into building designs to improve energy efficiency and empower decision-making and sustainability in buildings.

Comparative Analysis of Paddy Cultivation Sustainability through Integrating Eco-efficiency and Best-Worst Method Approaches

To investigate the sustainability of agricultural systems, it is necessary to prioritize production systems from various viewpoints. The current research, as a new approach, aimed at prioritizing different paddy cultivation areas in Iran based on sustainability criteria using the best-worst method (BWM), which is regarded as one of the modern multi-criteria decision-making techniques. Mazandaran, Fars, and Khuzestan areas in Iran with different climatic classes, i.e., Mazandaran, Fars, and Khuzestan, were considered in this study. Sustainability criteria included energy, exergy, economic, and environmental indices, and the prioritization process was done based on the eco-efficiency index and BWM method. In the evaluation of the eco-efficiency index, the amount of net income and the total environmental index were considered as economic and environmental indices, respectively. In the prioritization of the BWM method, ranking was done based on energy and exergy efficiency, benefit-cost ratio, and the total environmental index. The results of the eco-efficiency index investigation demonstrated that paddy production in Mazandaran province had the most sustainability and compatibility with the environment, followed by Fars and Khuzestan provinces, respectively. The results of the BWM method prioritization also indicated that Mazandaran province was the best and most sustainable area with a final index of 0.98, followed by Fars and Khuzestan provinces, which were placed in the next ranks of sustainable paddy cultivation areas with final indices of 0.93 and 0.85, respectively. In conclusion, the paddy production systems located in the humid subtropical region were found as the most sustainable systems in this area.

Organisms with high dispersal ability as a proxy for biogeographical characterisation of the Mediterranean biome

The Mediterranean biome offers a diverse range of habitats, particularly rocky environments, known for their long-term stability and persistence. Adapted to these rocky surfaces, chasmophytes thrive in this region, benefiting from reduced competition and human interference. The distribution of stress-tolerant cryptogams with generally broad niches represents a significant gap in the biogeography of the region. This study investigates whether complex symbiotic organisms with a potential for long-distance dispersal could serve as biogeographic indicators for delimitation of the Mediterranean biome's boundaries. Through comprehensive chorology data research, saxicolous lichen congeners of the genus Solenopsora were mapped across the Mediterranean Basin as a model area. Environmental predictors (climate and geology) were employed to predict suitable habitats, shedding light on the lichens' ecological niches. Habitat suitability models consistently aligned with the Mediterranean's geographical area, indicating a tight link between lichen distribution and Mediterranean climate classification. While climate change may pose vulnerabilities, no significant range shifts beyond the latitudinal limit of the Mediterranean were predicted. Despite overlapping climatic niches, differences in microhabitat preferences in terms of insolation were observed among studied taxa. Vulnerable regions with potential habitat decreases were identified under future scenarios, emphasizing the importance of environmental heterogeneity in securing local species persistence. This study supports the hypothesis that these lichen symbiotic systems are restricted to the Mediterranean Basin, highlighting their potential to reflect Mediterranean climate, environmental stability, and indicate the boundaries of the Mediterranean biome. Our investigation demonstrates that collection-based research can yield significant contributions to current biogeography.

Paleobotanical Evidence for Mediterranean Climates in the Western Canadian Paleoarctic During the Late Middle Eocene

AbstractPaleogene age deposits east of the Fifteenmile River, northwest of Dawson City, Yukon, Canada preserve a diverse high‐latitude fossil flora. Here, we provide new data on the age of the fossil site based on laser ablation–inductively coupled plasma–mass spectrometry (LA‐ICP‐MS) U‐Pb dating of tephra zircons, paleobotanical paleoclimate reconstructions, and growing season length estimates based on photoperiod. These new data indicate an age of the Fifteenmile River fossil locality as late middle Eocene and likely within the Middle Eocene Climatic Optimum episode. The paleoflora‐based paleoclimate reconstruction indicates the region was relatively wet and warm with non‐freezing winters, but also experienced seasonal dryness, with an approximate 7 months long growing season as suggested by photoperiod. We interpret this paleoclimate as summer dry and winter wet—a climate analogous to modern day warm Mediterranean climates in the Köppen‐Geiger climate classification system. These findings provide a new perspective on the past climate and environment of high‐latitude ecosystems during warm greenhouse intervals and contribute to our understanding of the Earth's climate history and its potential future changes.

Climate Classification in the Canadian Prairie Provinces Using Day-to-Day Thermal Variability Metrics

Climate Classification in the Canadian Prairie Provinces Using Day-to-Day Thermal Variability Metrics

Revisiting Iran's climate classification: A fresh perspective utilizing the köppen-geiger method

Revisiting Iran's climate classification: A fresh perspective utilizing the köppen-geiger method

Development of Statistical Downscaling Model Based on Volterra Series Realization, Principal Components, Climate Classification, and Ridge Regression

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin.

Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies

Abstract Wildfires pose a significant ecological, environmental, and socioeconomic challenge in southeastern Europe. The preservation of wildlands is not only essential but also a foremost priority for Montenegro, a country recognized as the world’s first ecological state. Consequently, the development of optimal methodologies and models is of paramount importance to enhance fire protection measures. With this objective in mind, this study strives to create a wildfire susceptibility model on a national scale for Montenegro. The study employed seven natural and anthropogenic causative criteria: vegetation type; aspect; slope; elevation; climate classification; distance from road; and population. The modeling process integrates both natural and anthropogenic causal criteria, employing the Fuzzy Analytic Hierarchy Process (F-AHP) and Frequency Ratio (FR) within geoinformatics environment. The outcomes of the F-AHP model reveal that 72.84% of the total area is categorized as having high to very high susceptibility. Conversely, based on the FR model, only 29.07% of the area falls within these susceptibility levels. In terms of validation, the area under curvature values indicates good performance of the F-AHP model. In contrast, the FR model demonstrates poor performance. These novel findings, pertaining to Montenegro at a national scale, offer valuable insights for preemptive wildfire safeguarding efforts. Moreover, the methodologies employed, with necessary modifications, hold potential for application in geographically diverse regions.

Typical earthworm assemblages of European ecosystem types

AbstractFor nature conservation and planning, terrestrial ecosystems are commonly classified based on their plant communities. Although soils are fundamental to ecosystem functioning, ecosystem classifications based on soil organisms are rare, and it is poorly understood whether their assemblage compositions follow existing classification schemes. We examined whether commonly used ecosystem types capture variation in earthworm (Lumbricidae) assemblages—a crucial biotic component of soil ecosystems. To this end, we created four ecosystem classifications by combining large‐scale climatic classifications (Biogeographic Regions [BGR] and Holdridge Life Zones [HLZ]) with small‐scale land cover classifications (CORINE Land Cover [CLC] and European Nature Information System [EUNIS]). European earthworm assemblage data from the sWORM and Edaphobase databases were analysed for variation in composition within and among ecosystem types, using Permutational Analysis of Variance and Analysis of Similarities. Additionally, we used Typical Species Analysis to establish typical earthworm assemblages (TAs) for each ecosystem type. Ecosystem classifications using the BGR explained more variance than HLZ, but HLZ showed a higher separation of assemblages between ecosystem types. The differentiation between Atlantic and Continental climates in the BGR could explain the superiority over the HLZ, which had only one category for the cool temperate zone of our study region. The typical assemblages contained on average six species, with some habitat generalists present in most. This study shows that combinations of ecosystem properties from different spatial scales can be used to distinguish between earthworm assemblages at the European level. However, earthworm assemblages across Europe were highly similar due to low species richness and the dominance of a few widespread species. This limits the possibility of applying TAs on large spatial scales, for example, for environmental monitoring. We suggest that future studies should explore the use of more species‐rich groups of soil organisms to characterize ecosystem types.

High‐resolution CMIP6 analysis highlights emerging climate challenges in alpine and Tibetan Tundra zones

AbstractWe employ a high‐resolution Köppen climate classification dataset to examine shifts in Tundra zones within the Alps and Asia. Our analysis shows substantial reductions in Tundra areas by the mid‐21st century under different Shared. Socioeconomic pathways (SSP1‐2.6, SSP3‐7.0, SSP5‐8.5). Tundra zones in the Alps and the Tibetan Plateau are crucial for their unique climates and role as water reservoirs. Characterized by short, mild summers and long, severe winters, these zones are vital for the glaciers and perennial snow. The projected climate instability may significantly reduce alpine snow cover by mid‐century with irreversible consequences. A 2°C temperature increase from the 1981–2010 baseline could eliminate the Tundra climate in the Alps and reduce it by over 70% in Asia. This is particularly concerning given that rivers from the Tibetan Plateau sustain nearly 40% of the global population.